# Python | Numpy numpy.resize()

With the help of Numpy numpy.resize(), we can resize the size of an array. Array can be of any shape but to resize it we just need the size i.e (2, 2), (2, 3) and many more. During resizing numpy append zeros if values at a particular place is missing.

Parameters:
new_shape : [tuple of ints, or n ints] Shape of resized array
refcheck : [bool, optional] This parameter is used to check the reference counter. By Default it is True.

Returns: None

Most of you are now thinking that what is the difference between reshape and resize. When we talk about reshape then an array changes it’s shape as temporary but when we talk about resize then the changes made permanently.

Example #1:
In this example we can see that with the help of `.resize()` method, we have changed the shape of an array from 1×6 to 2×3.

 `# importing the python module numpy ` `import` `numpy as np ` ` `  `# Making a random array ` `gfg ``=` `np.array([``1``, ``2``, ``3``, ``4``, ``5``, ``6``]) ` ` `  `# Reshape the array permanently ` `gfg.resize(``2``, ``3``) ` ` `  `print``(gfg) `

Output:

```[[1 2 3]
[4 5 6]]
```

Example #2:
In this example we can see that, we are trying to resize the array of that shape which is type of out of bound values. But numpy handles this situation to append the zeros when values are not existed in the array.

 `# importing the python module numpy ` `import` `numpy as np ` ` `  `# Making a random array ` `gfg ``=` `np.array([``1``, ``2``, ``3``, ``4``, ``5``, ``6``]) ` ` `  `# Required values 12, existing values 6 ` `gfg.resize(``3``, ``4``) ` ` `  `print``(gfg) `

Output:

```[[1 2 3 4]
[5 6 0 0]
[0 0 0 0]]
```
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